Valsan, Arun

Introduction: Although the role of cardiometabolic risk factors (CMRF) has been characterized in steatotic liver disease, their role in the severity of alcohol-associated hepatitis (AH) remains unclear. We aimed to evaluate the impact of CMRF on mortality. Methods: Multinational prospective cohort study (2015–2024) including hospitalized patients with severe AH across 32 centers in 14 countries. Diagnosis of AH was made using the NIAAA criteria. Analyses included adjusted competing-risk models by age, sex, ethnicity, history of cirrhosis, CMRF, corticosteroid use, MELD, and ACLF grade, with liver transplantation as a competing risk. Results: 936 participants were included; mean age 48±11.2 years, and 88.9% were male. At least one CMRF was present in 46.6%; median body mass index (BMI) was 24.2 kg/m 2 [IQR: 22.8–28.2], prevalence of diabetes 17.6%, hypertension 16.5%, and dyslipidemia 5.8%. Median MELD was 24.4 [19.3–31.4], 86.7% had severe AH, and 180-day survival was 72.9%. Survival did not differ by CMRF status (log-rank p =0.453). In adjusted competing-risk models, higher age (sHR 1.02; 95%CI:1.01–1.04), greater alcohol intake (per g/day; sHR 1.001; 95%CI:1.000–1.002), MELD (sHR 1.04; 95%CI:1.01–1.06), and ACLF grade 2–3 (sHR 2.34 and 4.34) predicted mortality. However, no individual CMRF independently increased mortality. A prespecified non-linear BMI analysis showed modestly lower mortality between 25–40 kg/m², with a higher risk above 40 kg/m². Conclusion: Among patients with severe AH, metabolic dysfunction was not associated with increased mortality. Although a higher BMI was associated with slightly lower mortality in AH, this may reflect better nutritional status rather than a true protective effect.

SummaryBackground and AimsMachine learning (ML) can identify the hidden patterns without hypothesis in heterogeneous diseases like acute‐on‐chronic live failure (ACLF). We employed ML to describe and predict yet unknown clusters in ACLF.MethodsClinical data of 1568 patients with ACLF from a tertiary care centre (2015–2023) were subjected to distance‐, density‐ and model‐based clustering algorithms. Final model was selected on best cluster separation, viz. Silhouette width and Dunn's index (for distance‐ or density‐based algorithms) and minimum BIC (for model‐based algorithms). Cluster assignments, patient trajectories and survival were analysed through inferential statistics. Supervised ML models were trained in 70% data that predicted clusters in remaining 30% data followed by an temporal validation.ResultsThe cohort was male‐predominant (87%), aged 44.3 years, with alcohol‐associated hepatitis (62.9%) and survival of 50.5%. Due to poor performance of distance‐ and density‐based algorithms and better explainability, the latent class model (LCM) was selected for exploration. LCM revealed four clusters with distinct trajectories, reversibility and survival (independent of MELD, CLIF‐C ACLF and AARC scores). Cluster1 had patients with none/one organ failure and highest reversibility. Cluster2 had females with viral hepatitis and two organ failures. More‐than‐one acute precipitant, severity, infections, organ failures and irreversibility escalated from clusters 1 to 4. Circulatory and renal failures critically influenced cluster assignments. Incorporating clusters to CLIF‐C ACLF, infection and ACLF definition improved the discriminative accuracy of CLIF‐C‐ACLF by 11%. Extreme gradient boost and decision trees could predict clusters with AUCs of 0.989 (0.979–0.995) and 0.875 (0.865–0.890). MELD, CLIF‐C‐OF, haemoglobin, lactate, CLIF‐C‐ACLF and ALT were critical variables for cluster prediction. Clusters with distinct survival were documented in a temporal validation cohort.ConclusionsML for the first time could identify clusters with distinct phenotypes, trajectories and outcomes in ACLF. Stratification into clusters can address heterogeneity, guide prognosis, recruitment in trials, resource allocation and liver transplant discussions in ACLF.